Carcinogenesis, progression of normal cells to malignant cancer, derives from hallmark capabilities of cancer driven by acquiring (somatic) mutations in driver genes with a selective advantage for cellular proliferation and potentially metastasis. A major motivation for modern cancer genomics studies is to decipher the genetic architecture of cancer by discovering new driver genes. The most widely-used approaches to predict and prioritize driver genes are based on statistics of mutation frequencies. Several methods have been proposed to identify genes with an excessive number of somatic mutations [9-11], known as significantly mutated genes. I propose to address two major limitations of this approach. First, these methods are insufficiently statistically powered given the amount of sequencing data currently available [15]. I will improve statistical power by leveraging diverse information in cancer genomics currently available into a developed machine learning method. Second, there is little objective clarity about the true effectiveness of these methods [11, 14], since there is no agreed-upon gold standard of driver genes, with the exception of a few well-known drivers. I will develop a framework to compare the effectiveness of driver gene prediction methods, in the absence of a gold standard. Both effectively and efficiently identifying cancer driver genes is a matter of great importance to science funding policy towards cancer genomics.